{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70a0534f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "def plot_loss(file_name):\n",
    "    train_loss = []   # This is actually psnr\n",
    "    val_loss = []     # This is actually loss\n",
    "    counter = 0\n",
    "    with open(file_name) as txtfile:\n",
    "         for row in txtfile:\n",
    "            curt_row = row.split(' ')\n",
    "            if counter >= 0:\n",
    "                if len(curt_row) >= 4 : \n",
    "                    if curt_row[3] == 'Train' :\n",
    "#                         print(curt_row[-1])\n",
    "                        train_loss.append(float(curt_row[-1][:-2]))\n",
    "                    if curt_row[3] == 'Eval':\n",
    "#                         print(curt_row[-1])\n",
    "                        val_loss.append(float(curt_row[-1][:-2]))\n",
    "            counter = counter + 1\n",
    "                    \n",
    "    print(len(train_loss))\n",
    "    train_loss = train_loss[:]\n",
    "    val_loss = val_loss[:]\n",
    "    plt.plot(val_loss)\n",
    "    plt.plot(train_loss)\n",
    "\n",
    "\n",
    "# resnet = './drrn_u9_model/train.log'\n",
    "# drrn_blu9_32 = './drrn_b1u9_model/train.log'\n",
    "# drrn_blu9_64 = './drrn_b1u9_filter_5_model/train.log'\n",
    "# drrn_blu9_128 = './drrn_b1u15_model/train.log'\n",
    "drrn_blu9 = './drrn_b1u9_model/loss.txt'\n",
    "\n",
    "# plot_loss(resnet)\n",
    "# plot_loss(drrn_blu9_32)\n",
    "# plot_loss(drrn_blu9_64)\n",
    "# plot_loss(drrn_blu9_128)\n",
    "plot_loss(drrn_blu9)\n",
    "\n",
    "# plt.plot(val_loss)\n",
    "plt.legend(['Val Loss','Train Loss'])\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.ylabel('Loss(MSE)')\n",
    "plt.title('B1U9 Avg Loss vs Num of Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70a0872208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "def plot_loss(file_name):\n",
    "    train_loss = []   # This is actually psnr\n",
    "    val_loss = []     # This is actually loss\n",
    "    counter = 0\n",
    "    with open(file_name) as txtfile:\n",
    "         for row in txtfile:\n",
    "            curt_row = row.split(' ')\n",
    "            if len(curt_row) >= 4 : \n",
    "                if curt_row[3] == 'Train' :\n",
    "#                     print(curt_row[-1])\n",
    "                    train_loss.append(float(curt_row[-1][:-2]))\n",
    "                if curt_row[3] == 'Eval':\n",
    "#                     print(curt_row[-1])\n",
    "                    val_loss.append(float(curt_row[-1][:-2]))\n",
    "                    \n",
    "\n",
    "    train_loss = train_loss[1:60]\n",
    "    val_loss = val_loss[1:60]\n",
    "    plt.plot(val_loss)\n",
    "    plt.plot(train_loss)\n",
    "    \n",
    "\n",
    "# resnet = './drrn_u9_model/train.log'\n",
    "# drrn_blu9_32 = './drrn_b1u9_model/train.log'\n",
    "# drrn_blu9_64 = './drrn_b1u9_filter_5_model/train.log'\n",
    "# drrn_blu9_128 = './drrn_b1u15_model/train.log'\n",
    "drrn_blu9 = './drrn_b1u9_model/train.log'\n",
    "\n",
    "# plot_loss(resnet)\n",
    "# plot_loss(drrn_blu9_32)\n",
    "# plot_loss(drrn_blu9_64)\n",
    "# plot_loss(drrn_blu9_128)\n",
    "plot_loss(drrn_blu9)\n",
    "\n",
    "# plt.plot(val_loss)\n",
    "plt.legend(['Val Loss','Train Loss'])\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.ylabel('Loss(MSE)')\n",
    "plt.title('ResNet Avg Loss vs Num of Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
